LogitBoost which was programmed to use 85 variables to calculate risks to a person's health who was complaining of chest pain. Patients had a coronary computed tomography angiography (CCTA) scan (pictured, stock scan) which gathered 58 of the data points

Algorithms similar to those employed by Netflix and Spotify to customise services are now better than human doctors at spotting who will die or have a heart attack.

Machine learning was used to train LogitBoost, which its developers say can predict death or heart attacks with 90 per cent accuracy.

It was programmed to use 85 variables to calculate the risk to the health of the 950 patients that it was fed scans and data from.

Patients complaining of chest pain were subjected to a host of scans and tests before being treated by traditional methods.

Their data was later used to train the algorithm.

It 'learned' the risks and, during the six-year follow-up, had a 90 per cent success rate at predicting 24 heart attacks and 49 deaths from any cause.

Services like Netflix and Spotify systems all use algorithms in a similar way to adapt to individual users and offer a more personalised look.

The 85 variables were entered into LogitBoost, which analysed them repeatedly until it found the best structure to predict who had a heart attack or died.

Dr Juarez-Orozco said: 'The algorithm progressively learns from the data and after numerous rounds of analyses, it figures out the high dimensional patterns that should be used to efficiently identify patients who have the event - the result is a score of individual risk.

'Doctors already collect a lot of information about patients - for example, those with chest pain.

'We found that machine learning can integrate these data and accurately predict individual risk.

'This should allow us to personalise treatment and ultimately lead to better outcomes for patients.'

The study was presented at The International Conference on Nuclear Cardiology and Cardiac CT.

Comment: While this appears to be another useful tool for medical diagnosis, the downside of such technology is that humans may come to rely solely on its prognostications and may fail to notice contradicting information.

The most successful tyranny is not the one that uses force to assure uniformity but the one that removes the awareness of other possibilities, that makes it seem inconceivable that other ways are viable, that removes the sense that there is an outside.